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40
Verification by abstract interpretation
 In Verification: Theory and Practice
, 2003
"... Dedicated to Zohar Manna, for his 2 6 th birthday. Abstract. Abstract interpretation theory formalizes the idea of abstraction of mathematical structures, in particular those involved in the specification of properties and proof methods of computer systems. Verification by abstract interpretation is ..."
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Cited by 243 (18 self)
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Dedicated to Zohar Manna, for his 2 6 th birthday. Abstract. Abstract interpretation theory formalizes the idea of abstraction of mathematical structures, in particular those involved in the specification of properties and proof methods of computer systems. Verification by abstract interpretation is illustrated on the particular cases of predicate abstraction, which is revisited to handle infinitary abstractions, and on the new parametric predicate abstraction. 1
On probabilistic model checking
, 1996
"... Abstract. This tutorial presents an overview of model checking for both discrete and continuoustime Markov chains (DTMCs and CTMCs). Model checking algorithms are given for verifying DTMCs and CTMCs against specifications written in probabilistic extensions of temporal logic, including quantitative ..."
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Cited by 106 (26 self)
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Abstract. This tutorial presents an overview of model checking for both discrete and continuoustime Markov chains (DTMCs and CTMCs). Model checking algorithms are given for verifying DTMCs and CTMCs against specifications written in probabilistic extensions of temporal logic, including quantitative properties with rewards. Example properties include the probability that a fault occurs and the expected number of faults in a given time period. We also describe the practical application of stochastic model checking with the probabilistic model checker PRISM by outlining the main features supported by PRISM and three realworld case studies: a probabilistic security protocol, dynamic power management and a biological pathway. 1
A.: Automatic verification of competitive stochastic systems
, 2011
"... Abstract. We present automatic verification techniques for the modelling and analysis of probabilistic systems that incorporate competitive behaviour. These systems are modelled as turnbased stochastic multiplayer games, in which the players can either collaborate or compete in order to achieve a p ..."
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Cited by 17 (12 self)
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Abstract. We present automatic verification techniques for the modelling and analysis of probabilistic systems that incorporate competitive behaviour. These systems are modelled as turnbased stochastic multiplayer games, in which the players can either collaborate or compete in order to achieve a particular goal. We define a temporal logic called rPATL for expressing quantitative properties of stochastic multiplayer games. This logic allows us to reason about the collective ability of a set of players to achieve a goal relating to the probability of an event’s occurrence or the expected amount of cost/reward accumulated. We give a model checking algorithm for verifying properties expressed in this logic and implement the techniques in a probabilistic model checker, based on the PRISM tool. We demonstrate the applicability and efficiency of our methods by deploying them to analyse and detect potential weaknesses in a variety of large case studies, including algorithms for energy management and collective decision making for autonomous systems. 1
Pareto curves for probabilistic model checking
 In Proc. 10th International Symposium on Automated Technology for Verification and Analysis (ATVA’12), LNCS
, 2012
"... Abstract. Multiobjective probabilistic model checking provides a way to verify several, possibly conflicting, quantitative properties of a stochastic system. It has useful applications in controller synthesis and compositional probabilistic verification. However, existing methods are based on linea ..."
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Cited by 10 (5 self)
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Abstract. Multiobjective probabilistic model checking provides a way to verify several, possibly conflicting, quantitative properties of a stochastic system. It has useful applications in controller synthesis and compositional probabilistic verification. However, existing methods are based on linear programming, which limits the scale of systems that can be analysed and makes verification of timebounded properties very difficult. We present a novel approach that addresses both of these shortcomings, based on the generation of successive approximations of the Pareto curve for a multiobjective model checking problem. We illustrate dramatic improvements in efficiency on a large set of benchmarks and show how the ability to visualise Pareto curves significantly enhances the quality of results obtained from current probabilistic verification tools. 1
Approximate verification of the symbolic dynamics of Markov chains. Technical report available at http://www.crans.org/˜genest/AAGT12.pdf
"... Abstract—A finite state Markov chain M is often viewed as a probabilistic transition system. An alternative view which we follow here is to regard M as a linear transform operating on the space of probability distributions over its set of nodes. The novel idea here is to discretize the probability ..."
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Cited by 9 (0 self)
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Abstract—A finite state Markov chain M is often viewed as a probabilistic transition system. An alternative view which we follow here is to regard M as a linear transform operating on the space of probability distributions over its set of nodes. The novel idea here is to discretize the probability value space [0,1] into a finite set of intervals. A concrete probability distribution over the nodes is then symbolically represented as a tuple D of such intervals. The ith component of the discretized distribution D will be the interval in which the probability of node i falls. The set of discretized distributions is a finite set and each trajectory, generated by repeated applications of M to an initial distribution, will induce a unique infinite string over this finite set of letters. Hence, given a set of initial distributions, the symbolic dynamics of M will consist of an infinite language L over the finite alphabet of discretized distributions. We investigate whether L
D.: Automated verification and strategy synthesis for probabilistic systems (extended version) (2013), available from [49
"... Abstract. Probabilistic model checking is an automated technique to verify whether a probabilistic system, e.g., a distributed network protocol which can exhibit failures, satisfies a temporal logic property, for example, “the minimum probability of the network recovering from a fault in a given ti ..."
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Cited by 5 (1 self)
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Abstract. Probabilistic model checking is an automated technique to verify whether a probabilistic system, e.g., a distributed network protocol which can exhibit failures, satisfies a temporal logic property, for example, “the minimum probability of the network recovering from a fault in a given time period is above 0.98”. Dually, we can also synthesise, from a model and a property specification, a strategy for controlling the system in order to satisfy or optimise the property, but this aspect has received less attention to date. In this paper, we give an overview of methods for automated verification and strategy synthesis for probabilistic systems. Primarily, we focus on the model of Markov decision processes and use property specifications based on probabilistic LTL and expected reward objectives. We also describe how to apply multiobjective model checking to investigate tradeoffs between several properties, and extensions to stochastic multiplayer games. The paper concludes with a summary of future challenges in this area. 1
Verifying team formation protocols with probabilistic model checking
 In Proc. CLIMA’11
, 2011
"... Abstract. Multiagent systems are an increasingly important software paradigm and in many of its applications agents cooperate to achieve a particular goal. This requires the design of efficient collaboration protocols, a typical example of which is team formation. In this paper, we illustrate how p ..."
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Cited by 5 (4 self)
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Abstract. Multiagent systems are an increasingly important software paradigm and in many of its applications agents cooperate to achieve a particular goal. This requires the design of efficient collaboration protocols, a typical example of which is team formation. In this paper, we illustrate how probabilistic model checking, a technique for formal verification of probabilistic systems, can be applied to the analysis, design and verification of such protocols. We start by analysing the performance of an existing team formation protocol modelled as a discretetime Markov chain. Then, using a Markov decision process model, we construct optimal algorithms for team formation. Finally, we use stochastic twoplayer games to analyse the competitive coalitional setting, in which agents are split into cooperative and hostile classes. We present experimental results from these models using the probabilistic model checking tool PRISM, which we have extended with support for stochastic games. 1
M.: Verification of partialinformation probabilistic systems using counterexampleguided refinements
 In: Proc. ATVA’12. LNCS, Springer (2012) Automated Verification and Strategy Synthesis for Probabilistic Systems 17
"... Abstract. The verification of partialinformation probabilistic systems has been shown to be undecidable in general. In this paper, we present a technique based on inspection of counterexamples that can be helpful to analyse such systems in particular cases. The starting point is the observation th ..."
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Cited by 4 (0 self)
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Abstract. The verification of partialinformation probabilistic systems has been shown to be undecidable in general. In this paper, we present a technique based on inspection of counterexamples that can be helpful to analyse such systems in particular cases. The starting point is the observation that the system under complete information provides safe bounds for the extremal probabilities of the system under partial information. Using classical (total information) model checkers, we can determine optimal schedulers that represent safe bounds but which may be spurious, in the sense that they use more information than is available under the partial information assumptions. The main contribution of this paper is a refinement technique that, given such a scheduler, transforms the model to exclude the scheduler and with it a whole class of schedulers that use the same unavailable information when making a decision. With this technique, we can use classical total information probabilistic model checkers to analyse a probabilistic partial information model with increasing precision. We show that, for the case of infimum reachability probabilities, the total information probabilities in the refined systems converge to the partial information probabilities in the original model. 1
Computing quantiles in Markov reward models
 In Proc. of FOSSACS, LNCS 7794
, 2013
"... Abstract. Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables. For the quantitative system analysis, however, there is also another type of interesting performance measure, nam ..."
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Abstract. Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables. For the quantitative system analysis, however, there is also another type of interesting performance measure, namely quantiles. A typical quantile query takes as input a lower probability bound p ∈]0, 1] and a reachability property. The task is then to compute the minimal reward bound r such that with probability at least p the target set will be reached before the accumulated reward exceeds r. Quantiles are wellknown from mathematical statistics, but to the best of our knowledge they have not been addressed by the model checking community so far. In this paper, we study the complexity of quantile queries for until properties in discretetime finitestate Markov decision processes with nonnegative rewards on states. We show that qualitative quantile queries can be evaluated in polynomial time and present an exponential algorithm for the evaluation of quantitative quantile queries. For the special case of Markov chains, we show that quantitative quantile queries can be evaluated in pseudopolynomial time. 1
Model checking of trustbased usercentric cooperative networks
 In AFIN 2012, The Fourth International Conference on Advances in Future Internet
, 2012
"... Abstract—The success of usercentric networks depends on the willingness of the participants to cooperate by sharing resources and services. Reputationbased incentives and remuneration (based either on fiat money or on virtual currency) have emerged as two complementary incentive mechanisms to inc ..."
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Cited by 2 (1 self)
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Abstract—The success of usercentric networks depends on the willingness of the participants to cooperate by sharing resources and services. Reputationbased incentives and remuneration (based either on fiat money or on virtual currency) have emerged as two complementary incentive mechanisms to increase users ’ motivation and to discourage selfish behaviors. In this paper, we conduct a formal study of the benefits of the joint application of these two mechanisms in the context of a cooperation model recently proposed for usercentric wireless networks. To this purpose, several performance properties of cooperation incentives mechanisms are defined and analyzed through model checking of probabilistic systems with an underlying Markov process semantics. Keywordstrust, virtual currency, model checking, usercentric networks. I.